Avro

Dependencies & cluster requirements

Dependencies:

ProphecySparkBasicsPython 0.0.1+
ProphecySparkBasicsScala 0.0.1+

Cluster requirements:

UC dedicated clusters 14.3+ supported
UC standard clusters 14.3+ supported
Livy clusters not supported

The Avro file type:

Is a row-based, open-source data serialization system.
Supports schema evolution, which allows schemas to evolve over time without breaking compatibility.
Stores the data in a binary format, which makes the data compact and efficient.
Usually appears in big data environments, data streaming applications, and scenarios where data serialization and storage are important.

Parameters

Parameter	Tab	Description
Location	Location	File path to read from or write to the Avro file.
Schema	Properties	Schema to apply on the loaded data. In the Source gem, you can define or edit the schema visually or in JSON code. In the Target gem, you can view the schema visually or as JSON code.

Source

The Source gem reads data from Avro files and allows you to optionally specify the following additional properties.

Source properties

Property name	Description	Default
Description	Description of your dataset.	None
Use user-defined schema	Whether to use the schema you define.	false
Ignore files without .avro extension while reading	DEPRECATED. Whether to load files without the `.avro` extension. This parameter will be removed in a future release. To filter file names, use the `Path Glob Filter` property.	true
Recursive File Lookup	Whether to recursively load files and disable partition inferring. If the data source explicitly specifies the `partitionSpec` when the`recursiveFileLookup` is `true`, the Source gem throws an exception.	false
Path Global Filter	Glob pattern to only include files with paths matching the pattern. The syntax follows GlobFilter and does not change the behavior of partition discovery.	None
Modified Before	Timestamp to only include files with modification times occurring before the time you specify. The timestamp must be in the following format: `YYYY-MM-DDTHH:mm:ss` (e.g. 2020-06-01T13:00:00)	None
Modified After	Timestamp to only include files with modification times occurring after the time you specify. The timestamp must be in the following format: `YYYY-MM-DDTHH:mm:ss` (e.g. 2020-06-01T13:00:00)	None
Avro Schema	Additional schema a user provides in JSON format. To learn more, see Schema evolution.	None

Schema evolution

When reading an Avro file, you can set the Avro Schema parameter to a newer evolved schema, which is compatible but different from the schema written to storage. The resulting DataFrame follows the newer evolved schema. For example, if you set an evolved schema to contain one additional column with a default value, the resulting DataFrame contains the new column too.

Example

Schema used in example above

Compiled code

To see the compiled code of your project, switch to the Code view in the project header.

def read_avro(spark: SparkSession) -> DataFrame:
 return spark.read\
 .format("avro")\
 .option("ignoreExtension", True)\
 .option(
 "avroSchema",
 "{\"type\":\"record\",\"name\":\"Person\",\"fields\":[{\"name\":\"firstname\",\"type\":\"string\"},{\"name\":\"middlename\",\"type\":\"string\"},{\"name\":\"lastname\",\"type\":\"string\"},{\"name\":\"dob_year\",\"type\":\"int\"},{\"name\":\"dob_month\",\"type\":\"int\"},{\"name\":\"gender\",\"type\":\"string\"},{\"name\":\"salary\",\"type\":\"int\"}]}"
 )\
 .load("dbfs:/FileStore/Users/abhinav/avro/test.avro")

object read_avro {

 def apply(spark: SparkSession): DataFrame =
 spark.read
 .format("avro")
 .option("ignoreExtension", true)
 .option(
 "avroSchema",
 "{\"type\":\"record\",\"name\":\"Person\",\"fields\":[{\"name\":\"firstname\",\"type\":\"string\"},{\"name\":\"middlename\",\"type\":\"string\"},{\"name\":\"lastname\",\"type\":\"string\"},{\"name\":\"dob_year\",\"type\":\"int\"},{\"name\":\"dob_month\",\"type\":\"int\"},{\"name\":\"gender\",\"type\":\"string\"},{\"name\":\"salary\",\"type\":\"int\"}]}"
 )
 .load("dbfs:/FileStore/Users/abhinav/avro/test.avro")

}

Target

The Target gem writes data to Avro files and allows you to optionally specify the following additional properties.

Target properties

Property name	Description	Default
Description	Description of your dataset.	None
Avro Schema	Additional schema a user provides in JSON format. You can set this parameter if the expected output Avro schema doesn’t match the schema Spark converts. For example, the expected schema of a column is an `enum` type, instead of a `string` type in the default converted schema.	None
Write Mode	How to handle existing data. For a list of the possible values, see Supported write modes.	`error`
Compression	Compression codec when writing to the Avro file. The Avro file supports the following codecs: `uncompressed`, `snappy`, `deflate`, `bzip2`, and `xz`. This defaults to the value of the `spark.sql.avro.compression.codec` parameter.	`snappy`
Partition Columns	List of columns to partition the Avro files by.	None
Record Name	Top level record name in the result.	`topLevelRecord`
Record Namespace	Record namespace in the result.	"" (empty string)

Supported write modes

Write mode	Description
error	If the data already exists, throw an exception.
overwrite	If the data already exists, overwrite the data with the contents of the `DataFrame`.
append	If the data already exists, append the contents of the `DataFrame`.
ignore	If the data already exists, do nothing with the contents of the `DataFrame`. This is similar to the `CREATE TABLE IF NOT EXISTS` clause in SQL.

Compiled code

To see the compiled code of your project, switch to the Code view in the project header.

def write_avro(spark: SparkSession, in0: DataFrame):
 in0.write\
 .format("avro")\
 .mode("overwrite")\
 .partitionBy("dob_year","dob_month")\
 .save("dbfs:/data/test_output.avro")

object write_avro {
 def apply(spark: SparkSession, in: DataFrame): Unit =
 in.write
 .format("avro")
 .mode("overwrite")
 .partitionBy("dob_year","dob_month")
 .save("dbfs:/data/test_output.avro")
}

Overview

Sources and targets

Data processing

Advanced

Parameters

Source

Source properties

Schema evolution

Example

Schema used in example above

Compiled code

Target

Target properties

Supported write modes

Compiled code

​Parameters

​Source

​Source properties

​Schema evolution

​Example

​Schema used in example above

​Compiled code

​Target

​Target properties

​Supported write modes

​Compiled code

Parameters

Source

Source properties

Schema evolution

Example

Schema used in example above

Compiled code

Target

Target properties

Supported write modes

Compiled code